NDLI: Performance of folded variance estimators for simulation

Content Provider	ACM Digital Library
Author	Alexopoulos, Christos Goldsman, David Meterelliyoz, Melike Antonini, Claudia
Copyright Year	2010
Abstract	We extend and analyze a new class of estimators for the variance parameter of a steady-state simulation output process. These estimators are based on “folded” versions of the standardized time series (STS) of the process, and are analogous to the area and Cramér--von Mises estimators calculated from the original STS. In fact, one can apply the folding mechanism more than once to produce an entire class of estimators, all of which reuse the same underlying data stream. We show that these folded estimators share many of the same properties as their nonfolded counterparts, with the added bonus that they are often nearly independent of the nonfolded versions. In particular, we derive the asymptotic distributional properties of the various estimators as the run length increases, as well as their bias, variance, and mean squared error. We also study linear combinations of these estimators, and we show that such combinations yield estimators with lower variance than their constituents. Finally, we consider the consequences of batching, and we see that the batched versions of the new estimators compare favorably to benchmark estimators such as the nonoverlapping batch means estimator.
Starting Page	1
Ending Page	36
Page Count	36
File Format	PDF
ISSN	10493301
e-ISSN	15581195
DOI	10.1145/1842713.1842714
Volume Number	20
Issue Number	3
Journal	ACM Transactions on Modeling and Computer Simulation (TOMACS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-10-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Steady-state simulation Folded estimators Method of batch means Simulation output analysis Standardized time series
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Modeling and Simulation

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